(1) Technical Field
The present invention relates to a space-invariant independent component analysis and electronic nose for detection of selective chemicals in an unknown environment, and more specifically to an approach to analysis of sensor responses to mixtures of unknown chemicals by an electronic nose in an open and changing environment.
(2) Background
The need for low-power, miniature sensor devices that can monitor air quality in an enclosed space with multi-compound capability and minimum human operation led to the development of a polymer-carbon composite based electronic nose (ENose) at NASA's Jet Propulsion Laboratory (JPL). The sensor array in the JPL ENose consists of 32 conductometric sensors made from insulating polymer films loaded with carbon. In its current design, it has the capability to detect 10 common contaminants which may be released into the recirculated breathing air of a space shuttle or space station released from a spill or a leak; target concentrations are based on the 1-hour Spacecraft Maximum Allowable Concentrations (SMAC) set by NASA, depicted in FIG. 1, and are in the parts-per-million (ppm) range. The ENose was intended to fill the gap between an alarm, which has little or no ability to distinguish among chemical compounds causing a response, and an analytical instrument, which can distinguish all compounds present but with no real-time or continuous event monitoring ability.
As in other array-based sensor devices, the individual sensor films of the ENose are not specific to any one analyte; it is in the use of an array of different sensor films that gases or gas mixtures can be uniquely identified by the pattern of measured response. The response pattern requires software analysis to deconvolute gas compounds and their concentrations.
An example sensor set is shown on the left in FIG. 2, with the complete assembled device shown on the right.
What is needed is a method of detection for selective chemicals as a result of leaks or spills of specific compounds. It has been shown in analysis of samples taken from space shuttle flights that, in general, air is kept clean by the air revitalization system and contaminants are present at levels significantly lower than the SMACs; the ENose has been developed to detect target compounds released suddenly into the breathing environment. A leak or a spill of a solvent or other compound would be an unusual event.
What is needed is an approach to analysis of sensor responses to mixtures of chemical compounds so that use of the ENose may by extended to detect chemical compounds in an open and changing environment, such as a building or a geographical area where air exchange is not controlled and limited. In an open environment, the collected sensory data will be comprised of a mixing between unknown chemicals with unknown mixing levels (coefficient) between them. The identification of chemical compounds among these mixing chemicals is a challenge for real world applications.
To determine whether a chemical compound exists in the an environment, one of the most well-known techniques is to recover the original chemicals. When done, the detection can be an easy step by determining the minimum phase between the predicted original reactants and the target chemicals. A more sophisticated method is to use a neural network approach, which can be employed to capture the target chemicals in various conditions, e.g., concentration levels through the parameterized weight set. Then, the strongest correlation between parameterized weight and the predicted original can be used to identify the intended chemical.
Recently, Independent Component Analysis (ICA) has proven effective to not only de-correlate second order statistics of the signals but also reduce higher order statistical dependencies. ICA transforms an observed signal vector into a set of signals that are as statistically independent as possible. Theoretically, ICA is an information-theoretic approach, which exploits concepts from information theory such as entropy and mutual information.
The ICA roots in the early work of Herault and Jutten who first introduced an adaptive algorithm in a simple feedback architecture that was able to separate several unknown independent sources. ICA was further developed, and recent improvements used natural gradient descent based on the Riemannian metric tensor to optimize the curvature of a particular manifold in n dimensional space. This technique is employed to apply to the Infomax to simplify the learning rule used here. ICA has applications for feature extraction in speech recognition systems, in communication systems, in medical signal processing, and in image processing.
Therefore, what is needed is an ICA method for detection of selective chemicals in an unknown environment using an electronic nose.